• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.376-376
• /
• 2022

전 세계적으로 홍수의 발생빈도가 증가함에 따라, 하천 내 홍수피해를 경감하기 위해 설치하는 하천시설에 대한 중요성이 강조되고 있다. 하천시설은 홍수조절, 이수를 위한 흐름의 제어와 유도, 자연환경의 유지 및 개선 등 중요한 역할을 하고 있으나, 구조적으로 물과의 접촉이 많아 물리적 손상이나 노후화가 매우 빠르게 진행되는 특성이 있다. 시설물의 노후화가 지속될수록 안정성을 보장하기 어려워 자연재난의 규모를 증가시킬 위험성이 있다. 하천시설의 선제적 유지관리를 위해, 본 연구에서는 시설물통합정보관리시스템(Facilty Management System; FMS)의 정밀안전진단 결과를 활용하여 시설물의 사용연수에 따른 성능지표의 변화를 기반으로 회귀식 형태의 성능평가모델을 개발하였다. 기존연구와의 비교를 통해 성능평가모델의 적합성을 확인하였으며, 개발한 성능평가모델은 하천시설의 생애주기를 통합적으로 고려함으로써 정량적인 상태를 예측할 수 있다는 장점이 있다. 본 연구에서 제안된 성능평가모델 결과는 하천시설의 생애주기 관리를 위한 기초자료로 활용 가능할 것으로 기대된다.

• Database Research
• /
• v.34 no.3
• /
• pp.124-136
• /
• 2018

There have been active research activities to use neural networks to analyze OCT images and make medical decisions. One requirement for these approaches to be promising solutions is that the trained network must be generalized to new devices without a substantial loss of performance. In this paper, we use a deep convolutional neural network to distinguish AMD from normal patients. The network was trained using a data set generated from an OCT device. We observed a significant performance degradation when it was applied to a new data set obtained from a different OCT device. To overcome this performance degradation, we propose an image normalization method which performs segmentation of OCT images to identify the retina area and aligns images so that the retina region lies horizontally in the image. We experimentally evaluated the performance of the proposed method. The experiment confirmed a significant performance improvement of our approach.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.4
• /
• pp.179-188
• /
• 2022

There have been many successful researches on a bearing fault diagnosis based on Deep Learning, but there is still a critical issue of the data distribution difference between training data and test data from their different working conditions causing performance degradation in applying those methods to the machines in the field. As a solution, a data adaptation method has been proposed and showed a good result, but each and every approach is strictly limited to a specific applying scenario or presupposition, which makes it still difficult to be used as a real-world application. Therefore, in this study, we have proposed a method that, using a data transformation with MFCCs and a simple CNN architecture, can perform a robust diagnosis on a target domain data without an additional learning or tuning on the model generated from a source domain data and conducted an experiment and analysis on the proposed method with the CWRU bearing dataset, which is one of the representative datasests for bearing fault diagnosis. The experimental results showed that our method achieved an equal performance to those of transfer learning based methods and a better performance by at least 15% compared to that of an input transformation based baseline method.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.32 no.3
• /
• pp.51-57
• /
• 2004

An intelligent performance diagnostic computer program of a gas turbine using the NN(Neural Network) was developed. Recently on-condition performance monitoring of major gas path components using the GPA(Gas Path Analysis) method has been performed in analyzing of engine faults. However because the types and severities of engine faults are various and complex, it is not easy that all fault conditions of the engine would be monitored only by the GPA approach Therefore in order to solve this problem, application of using the NNs for learning and diagnosis would be required. Among then, a BPN (Back Propagation Neural Network) with one hidden layer, which can use an updating learning rate, was proposed for diagnostics of PT6A-62 turboprop engine in this work.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.9
• /
• pp.4455-4461
• /
• 2013

This paper investigates the diagnosis performance of Artificial Neural Network (ANN) depending on the structure and the input vector type of ANN, which has been used to detect the partial discharge to lead to the electric machinery deterioration. The diagnosis performance of one hidden layer and two hidden layer in ANN are compared. The performance using the 2048 time-series data and the performance using the feature input vector are compared. For measuring the partial discharge signal, the tip-to-plate, the sphere-to-sphere, the tip-to-tip, the tip-to-sphere and the sphere-to-plate electrodes are used respectively. For ANN's learning, Matlab and C-code program are used. For evaluating the diagnosis performance of ANNs, the simulation studies are performed.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.49 no.4
• /
• pp.311-320
• /
• 2021

A study is performed for the real time fault diagnosis during operation and health estimation relating to performance deterioration in a turbojet engine used for an unmanned air vehicle. For this study the real time dynamic model is derived from the transient thermodynamic gas path analysis. For real fault conditions which are manipulated for the simulation, the detection techniques are applied such as Kalman filter and probabilistic decision-making approach based on statistical hypothesis test. Thereby the effectiveness is verified by showing good fault detection and isolation performances. For the health estimation with measurement parameters, it shows using an assumed performance degradation that the method by adaptive Kalman filter is feasible in practice for a condition based diagnosis and maintenance.

• Journal of the Korean Society of Radiology
• /
• v.18 no.5
• /
• pp.541-549
• /
• 2024

The various structures of artificial neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been extensively studied and served as the backbone of numerous models. Among these, a transformer architecture has demonstrated its potential for natural language processing and become a subject of in-depth research. Currently, the techniques can be adapted for image processing through the modifications of its internal structure, leading to the development of Vision transformer (ViT) models. The ViTs have shown high accuracy and performance with large data-sets. This study aims to develop a ViT-based model for detecting pneumonia using chest X-ray images and quantitatively evaluate its performance. The various architectures of the ViT-based model were constructed by varying the number of encoder blocks, and different patch sizes were applied for network training. Also, the performance of the ViT-based model was compared to the CNN-based models, such as VGGNet, GoogLeNet, and ResNet. The results showed that the traninig efficiency and accuracy of the ViT-based model depended on the number of encoder blocks and the patch size, and the F1 scores of the ViT-based model ranged from 0.875 to 0.919. The training effeciency of the ViT-based model with a large patch size was superior to the CNN-based models, and the pneumonia detection accuracy of the ViT-based model was higher than that of the VGGNet. In conclusion, the ViT-based model can be potentially used for pneumonia detection using chest X-ray images, and the clinical availability of the ViT-based model would be improved by this study.

• Proceedings of the Optical Society of Korea Conference
• /
• 1991.06a
• /
• pp.150-155
• /
• 1991

연소기체의 진단연구에 응용하기 위한 코헤런트 라만분광기를 설계·제작하여 성능을 조사하였다. 광원으로는 단일 종모드로 발진하는 Nd:YAG 레이저의 이차고조파와 색소 레이저를 이용하였다. Collinear 방법으로 상온의 질소기체 압력을 변화시키면서 분광 신호를 측정하였으며, 제작된 분광기 성능에 대하여 논의하였다.

• Proceedings of the KIEE Conference
• /
• 2004.10a
• /
• pp.245-247
• /
• 2004

고속철도는 수많은 하이테크 기술의 결정체이며, 이 중에서도 추진장치는 차량의 성능을 결정하는 매우 중요한 요소이다. 이러한 전장품들에 대한 다양한 성능을 평가하고 진단하기 위해 상시계측시스템을 구축하여 활용하고 있다. 이러한 계측장비들은 여러 전장품에 대한 계측 및 분석을 통한 시험평가와 동시에 완성차 시험이나 본선시운전 시험시에 발생할 수 있는 고장원인을 찾아내고 해결하는데 많은 도움을 주고 있다. 본 논문에서는 강시계측 시스템을 통해 추진장치에 대한 고장진단을 실시한 내용에 대하여 기술하였다.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2000.04a
• /
• pp.427-434
• /
• 2000

사례기반 추론은 사후학습기법이기 때문에, 사례베이스의 크기가 커지면 추론의 수행시간이 증가하여 전체적인 성능을 저하시킨다. 본 연구에서는 이러한 단점을 극복하기 위하여 사례기반 시스템의 구현에 앞서 사례들이 저장되어 있는 사례베이스를 클러스터링 하였다. 클러스터링에 사용한 기법은 부분적 매칭에 의한 유사도를 기준으로 클러스터링을 하는 사례기반 클러스터링 기법이다. 도출된 클러스터 각각에 대해 가장 적합한 사례기반 시스템을 구축하여 고장진단의 분야에 적용하였다. 즉, 새로운 고장 사례가 입력되었을 때에 본 연구에서 구축된 시스템에서는 먼저 해당 클러스터를 판별한 후 그 클러스터에 적합한 사례기반 시스템으로 고장진단을 하게 되는 것이다. 그 결과, 하나의 사례기반 시스템을 구축하였을 때보다 수행시간이 감소하였으며, 적중률도 향상되었다.